Welcome to R!

All artwork by Allison Horst (@allisonhorst) unless otherwise noted.

Why should you learn R?

It’s…

totally free!
open-source
a scripting language (reproducible and transparent)
an established tool with tons of tutorials and help pages
an amazing and inclusive community

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(1) First steps

R = programming language that can be written directly into your computer’s console/terminal

R-Studio = integrated development environment (IDE) with lots of features

Almost all R-users have both installed

R-Studio

Four panels:

Top-left: scripting panel – write and save code (may only open when you open a new file)

Bottom-left: console panel – input/output that won’t be saved

Top-right: environment panel – dataframes, variables (and sometimes has other features too, depending on what you’re working with)

Bottom-right: install and update packages, preview plots, read help files, and some other features

R script (.R)

File -> New File -> R Script

simple: only code
write lines of code in the order they are meant to be run
run each line by clicking that line and pressing Control + R (on Windows) or Cmd + Enter (on Mac)
output shows below, in console panel (number in bracket shows amount of output if there is more than one line)
Try it out: 5 + 5
comments that are not read as code: #

R-Markdown file (.Rmd)

File -> New File -> R Markdown

R-Markdown: more complex, both code and text, can also include images and some basic formatting (## for headers)
YAML information at top:

text in whitespace = text (unlike in an R script)
- keep notes or leave a record of your decisions
code needs a code chunk – type it with three backticks or use Insert in the top bar and select R

Run code: - press Control + R (on Windows) or Cmd + Enter (on Mac) with your cursor in the line - use green right-pointing arrow at right edge of code block - arrow with line under it -> runs all code chunks above (but not the current one)

Output: - directly below code block in preview window

Can also use (#) to denote lines that are comments and shouldn’t be read as R code

Try it out

Add a new (R) code block below this text. One of R’s most basic functions is as a (fancy) calculator. Try the following operations: 542 + 345, 67 * 29, 143/3
What happens if you chain together multiple operations? Try: 12 + 2 * 2 and (12 + 2) * 2 in an additional code block
Can you figure out what the following symbols mean in R: ^, %% ? Add another code block and give them a try. (Hint: %% is related to /)

(2) Variables

Simple variables

Variables: give name/label to output, saves it (can be used anywhere below in current script)

name_of_variable <- code

For example:

num_cats <- 12

num_cats * 3

## [1] 36

num_dogs <- 7

num_pets <- num_cats + num_dogs

num_pets

## [1] 19

double_cats <- num_cats * 2
double_cats

## [1] 24

Variables are case sensitive (caps/lowercase matters) and can’t include spaces.
Commonly snake_case
You can preview your current variables in the R-Studio environment pane

Try it out

You are planning a pizza party and want to figure out how many pieces of pizza each of your guests can eat before the pizzas run out. Create a variable called “guests” and assign it to the number of guests you have at your party: 15. (First, make a code block under this question!)
You order 5 pizzas. Assign the variable “pizzas” to the amount of pizzas you have: 6. Each pizza has 12 slices. Assign the variable “slices” to the amount of slices you have over all pizzas (hint: multiple the slices per pizza by the total number of pizzas – you can use the pizzas variable here instead of retyping the number)
Divide your “slices” variable by your “guests” variable to figure out how many slices each person can have.
5 more people arrive to the party uninvited. Update your “guests” variable to the total number you have now. How many slices can each person have now?

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Vectors

If you want to have multiple items of the same type: - Use a vector – enclosed in the syntax c()

command length() returns number of items in the vector

my_numbers <- c(12, 14, 26, 57, 82)

length(my_numbers)

## [1] 5

You can also do “vector-wise” operations:

my_numbers + 2

## [1] 14 16 28 59 84

my_numbers ^ 2

## [1]  144  196  676 3249 6724

my_numbers / 10

## [1] 1.2 1.4 2.6 5.7 8.2

Mathematical operation is “broadcast” to each item

Add/subtract/multiply/divide a vector by another vector: items matched up one at a time in a loop

my_numbers + my_numbers

## [1]  24  28  52 114 164

You can also take a certain item from a vector using square brackets. For example, take the first item:

my_numbers[1]

## [1] 12

Try it out

Make a variable called long_array and assign it the values 1, 1, 2, 2, 5, 10 (don’t forget the c() syntax!)
Make a variable called short_array and assign it the values 1, 6, 10
Subtract short_array from long_array
Subtract long_array from short_array
Look at the output. What does this tell you about how R deals with vectors of different lengths?

(3) Data types

Data types – how R interprets the input you give it
Figure out what type something is with class()

Numeric

class(8.8)

## [1] "numeric"

class(4.5113)

## [1] "numeric"

class(2)

## [1] "numeric"

fav_number <- 12

class(fav_number)

## [1] "numeric"

Numbers generally treated as numeric -> can include decimals

Integer

integer allows only whole numbers -> can force numbers into this form with as.integer()
- this will truncate the decimal (cut it off), not round

as.integer(8.8)

## [1] 8

integer_variable <- as.integer(8.8)

integer_variable

## [1] 8

class(integer_variable)

## [1] "integer"

Character

character is text, always enclosed in either double or single quotation marks

class("hello")

## [1] "character"

Uncomment the following line by deleting the hashtag (#). Check out the error here – it may sound confusing but try to identify what it is telling you.

my_word <- "hello"
class(my_word)

## [1] "character"

#my_word + 2

If you forget the quotes, you’ll also get an error (have to remove the comment character here to see it!)

#my_name <- Kyla

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Logical

logical can be either TRUE or FALSE, nothing else.

test if something is identical with == test if something is not identical with != test if something is greater than with > or less than with < or try greater than or equals to >= or less than or equal to with <=

7 < 8

## [1] TRUE

"dog" == "dogs"

## [1] FALSE

8.8 >= 5.6

## [1] TRUE

"seven" == 7

## [1] FALSE

class("seven" == 7)

## [1] "logical"

Source unknow

Factor

factor - string variables that should be treated as categories / distinct levels of a grouping -> will become more relevant when we work with datasets

Allows you to represent text as group labels, that multiple items can belong to

fav_flavors <- c("chocolate", "vanilla", "strawberry", "strawberry", "chocolate", "mint chocolate chip", "chocolate",  "vanilla", "strawberry", "chocolate")

class(fav_flavors)

## [1] "character"

summary(fav_flavors)

##    Length     Class      Mode 
##        10 character character

Change to factor with as.factor()

fav_flavors <- as.factor(fav_flavors)

class(fav_flavors)

## [1] "factor"

summary(fav_flavors)

##           chocolate mint chocolate chip          strawberry             vanilla 
##                   4                   1                   3                   2

Can also see the possible groupings of a factor using levels()

levels(fav_flavors)

## [1] "chocolate"           "mint chocolate chip" "strawberry"         
## [4] "vanilla"

Try it out

Assign your name to the variable “my_name”. What data type is the variable?
Try the command nchar() on your name variable. What does it do? Try also toupper().
What does nchar(my_name) >= 4 return? What does this mean?

Bonus: What does TRUE == 1 return? What about FALSE == 1? Try them both with 0 too. What does this tell you about how R deals with logical variables?

(4) Packages

Installing packages

Packages - extend basic R functions to add more complicated/specialized functionality

CRAN: R Consortium’s host for packages – any advanced R-programmer can make and submit their packages, any R-user can download and use them
Install packages once when you first use them
- command in console (or Markdown): install.packages("packagename")
- or use the panel at the lower right, under the Packages tab

Try it now with one of the most used packages by copying the following code into your console install.packages("tidyverse")

Using packages

library() command at beginning of script to use calls from that package – will only work if you’ve already installed the package
Good style: load all packages in first code block
Error about not finding a package? You probably don’t have it installed.

library(tidyverse)

## ── Attaching packages ──────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.0
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ─────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

In summary: You need to install a package once, but need to load it (using the library command) every time you start a new R session.

Try it out

Try the following command:

#beep(2)

What error message do you get?

Now install the package “beepr” and call the package to this session with a library call somewhere above the beep command (line above is enough.) Try running the command again (make sure your speakers are on – but not too loud :D)

Install and -load- the “cowsay” package and try the following command:

#say(what = "Good luck learning R!", by = "chicken")

(5) Reading in data

What type of file are you working with?
- Excel: go to File -> Save As and select either comma-separated values (csv) or tab-separated values (tsv)
- Or: open the file in a text editor to see what is used to separate the columns
Where is the file saved?
- working directory: the location that R should start looking for files – setwd()
  - ex: setwd(~Users/kyla/Documents...) or setwd(C:/Documents...)
  - can also use Session -> Set Working Directory -> Choose Directory…
- R-Markdown automatically sets the working directory to the folder you’re in
- once you’ve set the working directory:
  - can use files with just their short names (“experiment_data.csv”)
  - can use data in a folder (“data/experiment_data.csv”)
  - can use ../ to go backwards one folder
Read in the file:
- base-R: read.csv() and read.tsv()
- tidyverse (improvements, reads as tibble instead of dataframe): read_csv() and read_tsv()
  - must have tidyverse installed and loaded with a library() call
  - note: read_csv2() for csv files in languages that use a comma as a decimal separator and thus a semicolon as a csv separator
  - alternatively: read_delim(file, delim = "")

Save the output to a variable.

Source unknown

jump_data <- read_csv("jump.csv")

## Parsed with column specification:
## cols(
##   participant = col_character(),
##   height_cm = col_double(),
##   jump_height = col_double()
## )

jump_data <- read_delim("jump.csv", delim = ",")

## Parsed with column specification:
## cols(
##   participant = col_character(),
##   height_cm = col_double(),
##   jump_height = col_double()
## )

(6) Exploring your data

Now you have a data file read in, but how do you see what’s in it?

head(): shows first six rows

head(jump_data)

## # A tibble: 6 x 3
##   participant height_cm jump_height
##   <chr>           <dbl>       <dbl>
## 1 A                 170          51
## 2 B                 165          46
## 3 C                 167          40
## 4 D                 159          45
## 5 E                 182          62
## 6 F                 174          50

Can change the amount of rows with the n argument

head(jump_data, n=3)

## # A tibble: 3 x 3
##   participant height_cm jump_height
##   <chr>           <dbl>       <dbl>
## 1 A                 170          51
## 2 B                 165          46
## 3 C                 167          40

Or: click name of dataframe in Environment tab - can also sort columns and Filter rows – just for viewing purposes - bit slow if you start having huge dataframes but often a good first look

summary(): call it on a dataframe to get each column and useful info based on the data type. For example, numeric columns will show the min, median, max and the quartiles (25% increments).

summary(jump_data)

##  participant          height_cm      jump_height   
##  Length:8           Min.   :159.0   Min.   :31.00  
##  Class :character   1st Qu.:164.2   1st Qu.:39.50  
##  Mode  :character   Median :166.0   Median :45.50  
##                     Mean   :168.0   Mean   :45.38  
##                     3rd Qu.:171.0   3rd Qu.:50.25  
##                     Max.   :182.0   Max.   :62.00

Try it out

Download the three “penguin_data” files and save them in the same location as this Markdown. They all contain the same data, but it’s saved in slightly different formats, so you need to read them each in with a different command. Make sure to save them to variables.
Take a look at your dataframe in the Environment tab.
Try the command colnames() on the variable you saved the dataframe to. What does it do?
We already tried the head() command, now try the tail() command. What does it do?

(7) Columns

specific column call with dataframe_name$column_name

R-Studio will helpfully suggest the column names as soon as it sees the $

jump_data$participant

## [1] "A" "B" "C" "D" "E" "F" "G" "H"

jump_data$height_cm

## [1] 170 165 167 159 182 174 162 165

Descriptive stats

Basic descriptive statistics for numeric columns – mean, median and range

mean(jump_data$height_cm)

## [1] 168

range(jump_data$height_cm)

## [1] 159 182

median(jump_data$height_cm)

## [1] 166

summary(jump_data$height_cm)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   159.0   164.2   166.0   168.0   171.0   182.0

Try it out

Use the penguin dataset. What is the range of flipper lengths?
Save the mean body mass to a variable called “average_bodymass”. This number is in grams. What would the equivalent be in kilograms? Hint: Divide by 1000.

Categorical columns

The penguins data contains information on several different penguin species. But how many, exactly? Let’s try to use ‘summary’ to extract that information:

penguins <- read_tsv("penguin_data_3.txt")

## Parsed with column specification:
## cols(
##   species = col_character(),
##   island = col_character(),
##   bill_length_mm = col_double(),
##   bill_depth_mm = col_double(),
##   flipper_length_mm = col_double(),
##   body_mass_g = col_double(),
##   sex = col_character(),
##   year = col_double()
## )

summary(penguins$species)

##    Length     Class      Mode 
##       344 character character

Summary on a character column shows that each entry is seen as different and not comparable.
This is a character (the default for text data)
But should be a factor
Factors represent groups (participant, conditions, etc.)
We can use class on a variable to have R show us which data type it is:

class(penguins$species)

## [1] "character"

class(penguins$bill_length_mm)

## [1] "numeric"

Change a character column into a factor using as_factor()
- Overwrite the original column

penguins$species <- as_factor(penguins$species)

class(penguins$species)

## [1] "factor"

summary(penguins$species)

##    Adelie    Gentoo Chinstrap 
##       152       124        68

Quick reference: check the environment tab upper-right

Try it out

Which other variables in the penguins data should be factors? Change them using the same code we used above.
How many entries from penguins who live on the “Dream” island are there?

(8) The pipe operator

The pipe %>% is a command from the magrittr package (which is included in tidyverse, so no need to load it explicitly). When the pipe is used, the output from the first line is automatically passed to the second line. Specifically, the output is used as the first argument of the next command. Use Control + Shift + M to automatically insert %>%

summary(penguins)

##       species       island          bill_length_mm  bill_depth_mm  
##  Adelie   :152   Length:344         Min.   :32.10   Min.   :13.10  
##  Gentoo   :124   Class :character   1st Qu.:39.23   1st Qu.:15.60  
##  Chinstrap: 68   Mode  :character   Median :44.45   Median :17.30  
##                                     Mean   :43.92   Mean   :17.15  
##                                     3rd Qu.:48.50   3rd Qu.:18.70  
##                                     Max.   :59.60   Max.   :21.50  
##                                     NA's   :2       NA's   :2      
##  flipper_length_mm  body_mass_g       sex                 year     
##  Min.   :172.0     Min.   :2700   Length:344         Min.   :2007  
##  1st Qu.:190.0     1st Qu.:3550   Class :character   1st Qu.:2007  
##  Median :197.0     Median :4050   Mode  :character   Median :2008  
##  Mean   :200.9     Mean   :4202                      Mean   :2008  
##  3rd Qu.:213.0     3rd Qu.:4750                      3rd Qu.:2009  
##  Max.   :231.0     Max.   :6300                      Max.   :2009  
##  NA's   :2         NA's   :2

# is equivalent to:
penguins %>% # the line break here is optional, but makes the code more readable
  summary()

##       species       island          bill_length_mm  bill_depth_mm  
##  Adelie   :152   Length:344         Min.   :32.10   Min.   :13.10  
##  Gentoo   :124   Class :character   1st Qu.:39.23   1st Qu.:15.60  
##  Chinstrap: 68   Mode  :character   Median :44.45   Median :17.30  
##                                     Mean   :43.92   Mean   :17.15  
##                                     3rd Qu.:48.50   3rd Qu.:18.70  
##                                     Max.   :59.60   Max.   :21.50  
##                                     NA's   :2       NA's   :2      
##  flipper_length_mm  body_mass_g       sex                 year     
##  Min.   :172.0     Min.   :2700   Length:344         Min.   :2007  
##  1st Qu.:190.0     1st Qu.:3550   Class :character   1st Qu.:2007  
##  Median :197.0     Median :4050   Mode  :character   Median :2008  
##  Mean   :200.9     Mean   :4202                      Mean   :2008  
##  3rd Qu.:213.0     3rd Qu.:4750                      3rd Qu.:2009  
##  Max.   :231.0     Max.   :6300                      Max.   :2009  
##  NA's   :2         NA's   :2

head(penguins, n = 4)

## # A tibble: 4 x 8
##   species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex  
##   <fct>   <chr>           <dbl>         <dbl>            <dbl>       <dbl> <chr>
## 1 Adelie  Torge…           39.1          18.7              181        3750 male 
## 2 Adelie  Torge…           39.5          17.4              186        3800 fema…
## 3 Adelie  Torge…           40.3          18                195        3250 fema…
## 4 Adelie  Torge…           NA            NA                 NA          NA <NA> 
## # … with 1 more variable: year <dbl>

# is equivalent to:
penguins %>% 
  head(n = 4)

## # A tibble: 4 x 8
##   species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex  
##   <fct>   <chr>           <dbl>         <dbl>            <dbl>       <dbl> <chr>
## 1 Adelie  Torge…           39.1          18.7              181        3750 male 
## 2 Adelie  Torge…           39.5          17.4              186        3800 fema…
## 3 Adelie  Torge…           40.3          18                195        3250 fema…
## 4 Adelie  Torge…           NA            NA                 NA          NA <NA> 
## # … with 1 more variable: year <dbl>

Try it out

Look at the last three lines of the penguins data (hint: tail). Use the base R command, then try to rewrite it with the pipe %>%

(9) Conclusion

Installed R, R-Studio
R-Markdown
variables
data types: numeric, integer, character, factor, vector
installing & loading packages
reading in files
looking at dataframes in R

Next time:
- tidyverse
- rename columns
- make new columns
- reorder
- filter based on condition
- etc.

Reading

As a follow-up to this tutorial, you might want to read chapters 3.3 - 3.9 in Navarro, Learning Statistics with R (p. 46 - 66).

Intro to R

Kyla McConnell & Julia Müller

12/21/2020

Welcome to R!

(1) First steps

R-Studio

R script (.R)

R-Markdown file (.Rmd)

Try it out

(2) Variables

Simple variables

Try it out

Vectors

Try it out

(3) Data types

Numeric

Integer

Character

Logical

Factor

Try it out

(4) Packages

Installing packages

Using packages

Try it out

(5) Reading in data

(6) Exploring your data

Try it out

(7) Columns

Descriptive stats

Try it out

Categorical columns

Try it out

(8) The pipe operator

Try it out

(9) Conclusion

Reading